target_distribution_data_check¶
Module Contents¶
Classes Summary¶
Checks if the target data contains certain distributions that may need to be transformed prior training to |
Contents¶
-
class
evalml.data_checks.target_distribution_data_check.TargetDistributionDataCheck[source]¶ Checks if the target data contains certain distributions that may need to be transformed prior training to improve model performance.
Methods
Returns a name describing the data check.
Checks if the target data has a certain distribution.
-
name(cls)¶ Returns a name describing the data check.
-
validate(self, X, y)[source]¶ Checks if the target data has a certain distribution.
- Parameters
X (pd.DataFrame, np.ndarray) – Features. Ignored.
y (pd.Series, np.ndarray) – Target data to check for underlying distributions.
- Returns
List with DataCheckErrors if certain distributions are found in the target data.
- Return type
dict (DataCheckError)
Example
>>> from scipy.stats import lognorm >>> X = None >>> y = lognorm.rvs(0.1, size=1000) >>> target_checdk = TargetDistributionDataCheck() >>> assert target_check.validate(X, y) == {"errors": [], "warnings": [{"message": "Target may have a lognormal distribution.", "data_check_name": "TargetDistributionDataCheck", "level": "warning", "code": "TARGET_LOGNORMAL_DISTRIBUTION", "details": {"shapiro-statistic/pvalue": '0.993/0.0'}}], "actions": [{'code': 'TRANSFORM_TARGET', 'metadata': {'column': None, 'transformation_strategy': 'lognormal', 'is_target': True}}]}
-